第一次跑动论文的代码 艾玛，太不容易了 终于上GPU版本的Pytorch了 论文代码 pytorch版本 tensorflow版本 参考 [1] DenseFuse: A Fusion Approach to Infrared
SE实现对比度增强 纹理信息的损失函数考虑到了红外图像目标信息，保留更多的高频部分，纹理细节更加丰富了 论文读的有点糙，欢迎大佬们指正 参考 [1] DIVFusion: Darkness-free infrared
看到这篇文章之前，我一直以为GAN和图像融合不会有什么关系，不得不说作者真的强，在看完百度百科的介绍之后，作者在我心里已经是神了
具体内容可以阅读原文==》https://arxiv.org/abs/1804.08361 或者可以参考博客讲解==》DenseFuse: A Fusion Approach to Infrared...science/article/pii/S1566253518301143 或者可以参考博客讲解==》FusionGAN: A generative adversarial network for infrared...参考 [1] Self-supervised feature adaption for infrared and visible image fusion
而是使用神经网络来融合特征信息 损失函数方面提出了新颖的损失函数，从而保证RFN有很好的特征融合效果 参考 [1] RFN-Nest: An end-to-end residual fusion network for infrared
参考 [1] Real-time infrared and visible image fusion network using adaptive pixel weighting strategy
一、自适应双平台直方图均衡算法 《A new adaptive contrast enhancement algorithm for infrared images based on double plateaus
=0; //红外接收标志，收到一帧正确数据后置1 u8 Infrared_RX_Buff[4];//红外代码接收缓冲区 sbit Infrared_GPIO=P3^2;//红外接收引脚--外部中断0 /...*/ void Infrared_Init(void) { Infrared_GPIO=1;//红外接收引脚默认保持高电平输出 TMOD&=0xF0; //清除配置 TMOD|...if(Infrared_RX_Flag) //接收到红外数据 { Infrared_RX_Flag=0; //清楚标志...printf("user1:%d,user2:%d\r\n",(int)Infrared_RX_Buff[0],(int)((u8)(~Infrared_RX_Buff[1])));...printf("key1:%d,key2:%d\r\n",(int)Infrared_RX_Buff[2],(int)((u8)(~Infrared_RX_Buff[3]))); }
第3步：编写红外探测模块，文件名为infrared.py，与树莓派基础实验28：红外避障传感器实验中的Python程序基本相同，只是设置了类，重构了程序。 infrared.py： #!.../usr/bin/env python3 #-*- coding: utf-8 -*- #本模块只含Infrared()一个类，用于红外避障模块测出是否有障碍物 #有障碍物时返回值0，无障碍物时返回值1...import RPi.GPIO as GPIO import time class Infrared(): InfraredPinLeft = 29 #左侧模块的输出连接树莓派29...= Infrared() infrared.setup() while True: infra_left_value,infra_right_value...= Infrared() #初始化红外避障实例 infrared.setup() #初始化红外避障引脚 def ultra_control(): '''超声波传感器控制'''
#define infrared_PORT GPIOD#define infrared_PIN GPIO_Pin_7 //设置GPIO 口#define infrared_RCC RCC_APB2Periph_GPIOD...#define INFRARED_STATE() GPIO_ReadInputDataBit(infrared_PORT,infrared_PIN)//读红外的状态void infrared_INIT(...void)//红外测试{ GPIO_InitTypeDef GPIO_InitStructure;//定义结构体变量 RCC_APB2PeriphClockCmd(infrared_RCC,...; //设置浮空输入 GPIO_InitStructure.GPIO_Speed=GPIO_Speed_50MHz; //设置传输速率 GPIO_Init(infrared_PORT,...就通过INFRARED_STATE()可以获得了，好吧上代码。
satellite’s Operational Land Imager 2 (OLI-2) captures observations of the Earth’s surface in visible, near-infrared..., and shortwave-infrared bands, while its Thermal Infrared Sensor 2 (TIRS-2) measures the thermal infrared...The image was built using infrared, red, and blue bands of the electromagnetic spectrum....atmospheric interference to best mirror on-the-ground conditions, as recorded here in the short-wave infrared..., near infrared and red bands of the electromagnetic spectrum.
ASTER can collect data in 14 spectral bands from the visible to the thermal infrared...., nadir pointing) 1 255 15 meters 0.780-0.860µm B04 SWIR_Band4 (short-wave infrared) 1 255 30 meters...infrared) 1 255 30 meters 2.185-2.225µm B07 SWIR_Band7 (short-wave infrared) 1 255 30 meters 2.235-2.285µm...B08 SWIR_Band8 (short-wave infrared) 1 255 30 meters 2.295-2.365µm B09 SWIR_Band9 (short-wave infrared...TIR_Band11 (thermal infrared) 1 4095 90 meters 8.475-8.825µm B12 TIR_Band12 (thermal infrared) 1 4095
geometric median. red 0* 10000* 0.630-0.690 μm Band red surface reflectance geometric median. near_infrared...0* 10000* 0.760-0.900 μm Band near infrared surface reflectance geometric median. shortwave_infrared..._1 0* 10000* 1.550-1.750 μm Band shortwave infrared 1 surface reflectance geometric median. shortwave_infrared..._2 0* 10000* 2.080-2.350 μm Band shortwave infrared 2 surface reflectance geometric median.
(); //得到低电平时间 if(time10000)return; //标准时间: 9000us time=Infrared_GetTime_H()...3.1 红外线解码.c #include "nec_Infrared.h" u8 InfraredRecvData[4]; //存放红外线解码接收的数据 u8 InfraredRecvState=0;...//0表示未接收到数据,1表示接收到数据 /* 函数功能: 红外线解码初始化(接收) */ void Infrared_RecvInit(void) { Infrared_Time6_Init...(); //得到低电平时间 if(time10000)return; //标准时间: 9000us time=Infrared_GetTime_H()..."key.h" #include "usart.h" #include "at24c02.h" #include "W25Q64.h" #include "spi.h" #include "nec_Infrared.h
green 15 meters 0.52 - 0.60 µm B3 Landsat 7 ETM+ red 15 meters 0.63 - 0.69 µm B4 Landsat 7 ETM+ near infrared...15 meters 0.77 - 0.90 µm B5 Landsat 7 ETM+ shortwave infrared 1 30 meters 1.55 - 1.75 µm B6_low_gain...Landsat 7 ETM+ low-gain thermal Infrared 1....Resampled from 60m to 30m. 30 meters 10.40 - 12.50 µm B6_high_gain Landsat 7 ETM+ high-gain thermal Infrared...Resampled from 60m to 30m. 30 meters 10.40 - 12.50 µm B7 Landsat 7 ETM+ shortwave infrared 2 30 meters
= machine.PWM(machine.Pin(infrared_led_pin), freq=1000, duty=0) # 定义LED控制函数 def set_led_brightness(..., 2, 5) elif rule == "rule2": set_led_brightness(warm_white_led, 768) blink_led(infrared_led...# 测试 apply_stimulus_rule("rule1") # 清理 warm_white_led.deinit() infrared_led.deinit() 那么也可以加入一些远程控制的功能...，使用串口： import machine import time import ustruct # 定义LED引脚 warm_white_led_pin = 19 # 适应您的硬件设置 infrared_led_pin...= machine.PWM(machine.Pin(infrared_led_pin), freq=1000, duty=0) # 初始化串口 uart = machine.UART(0, baudrate
30 meters 0.45 - 0.52 µm 20 Green 30 meters 0.52 - 0.60 µm 30 Red 30 meters 0.63 - 0.69 µm 40 Near infrared...30 meters 0.76 - 0.90 µm 50 Shortwave infrared 1 30 meters 1.55 - 1.75 µm 60 Thermal Infrared 1....Resampled from 60m to 30m. 60 meters 10.40 - 12.50 µm 70 Shortwave infrared 2 30 meters 2.08 - 2.35 µm
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